Determining the Precision of Dual Energy X-Ray Absorptiometry and Bioelectric Impedance Spectroscopy in the Assessment of Breast Cancer-Related Lymphedema
Bibliographic record
Abstract
BACKGROUND: The composition of breast cancer-related lymphedema (BCRL) has been shown to evolve from the initial accumulation of fluid to the development of fibrotic lesions and abnormal fat deposition. Therefore, precise and reliable assessments of BCRL are required to develop accurate staging and management. Although dual energy x-ray absorptiometry (DXA) and bioelectric impedance spectroscopy (BIS) have been used to assess BCRL, no study has evaluated the precision of these two modalities in the same cohort. METHODS AND RESULTS: We determined the precision of DXA and BIS in lymphedematous (LE) and nonaffected (NA) arms of 24 women with Stage II unilateral BCRL. Precision was calculated from the results of paired bilateral arm measurements obtained from DXA scans measuring fat, lean, and bone mineral masses, BIS measuring extracellular fluid (ECF) and total fluid volume, and circumferential tape measurements (CM) of the arms to calculate the anatomic volume. Precision error was expressed as the root mean square (RMS) of the coefficients of variation (%CV) and standard deviations (SD). RESULTS: The precisions of DXA and BIS varied from 1.16% (DXA measurements of LE arm total volume) to 1.86% (BIS LE arm total fluid volume) and from 0.95% (DXA lean mass of NA arm) to 1.72% (DXA BMC of NA arm). Precision of CM measures of arm volume were 1.71% CV for LE arm and 2.51% CV for NA arm. The fat and lean masses of the LE arm exceeded the NA arm by about 15% (p<0.0001). ECF and total fluid volume of LE arm was 22.6% and 19% greater than the NA arm (p<0.0001), respectively. CONCLUSION: For BCRL, these findings suggest that DXA and BIS are two measurement instruments that provide acceptable levels of precision for the measurement of arm lean mass, fat mass and ECF volume, respectively.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".